{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、读入数据\n",
    "2、先训练knn\n",
    "3、训练bp神经网络\n",
    "4、训练决策树\n",
    "5、使用遗传算法,最后得到优化的参数\n",
    "6、最后从最后的个体中，选取最优的21个个体，进行集成学习，进行幸福感预测\n",
    "7、最后写报告整理资料、写课堂总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 一、读入数据、数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关的数据读入模块\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "plt.rcParams['font.sans-serif'] = ['KaiTi']\n",
    "plt.rcParams['font.serif'] = ['KaiTi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>happiness</th>\n",
       "      <th>survey_type</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>survey_time</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth</th>\n",
       "      <th>nationality</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbor_familiarity</th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>59</td>\n",
       "      <td>2015-08-04 14:18:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>52</td>\n",
       "      <td>85</td>\n",
       "      <td>2015-07-21 15:04:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>85.0</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>83</td>\n",
       "      <td>126</td>\n",
       "      <td>2015-07-21 13:24:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>79</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>51</td>\n",
       "      <td>2015-07-25 17:33:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1943</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>2015-08-10 09:50:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  happiness  survey_type  province  city  county         survey_time  \\\n",
       "0   1          4            1        12    32      59 2015-08-04 14:18:00   \n",
       "1   2          4            2        18    52      85 2015-07-21 15:04:00   \n",
       "2   3          4            2        29    83     126 2015-07-21 13:24:00   \n",
       "3   4          5            2        10    28      51 2015-07-25 17:33:00   \n",
       "4   5          4            1         7    18      36 2015-08-10 09:50:00   \n",
       "\n",
       "   gender  birth  nationality  ...  neighbor_familiarity  public_service_1  \\\n",
       "0       1   1959            1  ...                     4                50   \n",
       "1       1   1992            1  ...                     3                90   \n",
       "2       2   1967            1  ...                     4                90   \n",
       "3       2   1943            1  ...                     3               100   \n",
       "4       2   1994            1  ...                     2                50   \n",
       "\n",
       "   public_service_2 public_service_3  public_service_4  public_service_5  \\\n",
       "0                60               50                50              30.0   \n",
       "1                70               70                80              85.0   \n",
       "2                80               75                79              80.0   \n",
       "3                90               70                80              80.0   \n",
       "4                50               50                50              50.0   \n",
       "\n",
       "   public_service_6  public_service_7  public_service_8  public_service_9  \n",
       "0                30                50                50                50  \n",
       "1                70                90                60                60  \n",
       "2                90                90                90                75  \n",
       "3                90                90                80                80  \n",
       "4                50                50                50                50  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入数据，解析survey_time列的值作为独立的日期列，指定字符集类型\n",
    "train = pd.read_csv('./data/happiness_train_complete.csv', parse_dates=['survey_time'], encoding='latin-1')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 140)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据的形态\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5, -8}\n"
     ]
    }
   ],
   "source": [
    "#查看所有的幸福度\n",
    "o=set(train['happiness'])\n",
    "print(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理异常值\n",
    "# 将happiness中的‘-8’无法回答，改为‘3’说不上幸福不幸福\n",
    "train['happiness'] = train['happiness'].replace(-8, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "happiness                  0\n",
       "survey_type                0\n",
       "province                   0\n",
       "city                       0\n",
       "county                     0\n",
       "survey_time                0\n",
       "gender                     0\n",
       "birth                      0\n",
       "nationality                0\n",
       "religion                   0\n",
       "religion_freq              0\n",
       "edu                        0\n",
       "edu_other               7997\n",
       "edu_status              1120\n",
       "edu_yr                  1972\n",
       "income                     0\n",
       "political                  0\n",
       "join_party              7176\n",
       "floor_area                 0\n",
       "property_0                 0\n",
       "property_1                 0\n",
       "property_2                 0\n",
       "property_3                 0\n",
       "property_4                 0\n",
       "property_5                 0\n",
       "property_6                 0\n",
       "property_7                 0\n",
       "property_8                 0\n",
       "property_other          7934\n",
       "                        ... \n",
       "m_political                0\n",
       "m_work_14                  0\n",
       "status_peer                0\n",
       "status_3_before            0\n",
       "view                       0\n",
       "inc_ability                0\n",
       "inc_exp                    0\n",
       "trust_1                    0\n",
       "trust_2                    0\n",
       "trust_3                    0\n",
       "trust_4                    0\n",
       "trust_5                    0\n",
       "trust_6                    0\n",
       "trust_7                    0\n",
       "trust_8                    0\n",
       "trust_9                    0\n",
       "trust_10                   0\n",
       "trust_11                   0\n",
       "trust_12                   0\n",
       "trust_13                   0\n",
       "neighbor_familiarity       0\n",
       "public_service_1           0\n",
       "public_service_2           0\n",
       "public_service_3           0\n",
       "public_service_4           0\n",
       "public_service_5           0\n",
       "public_service_6           0\n",
       "public_service_7           0\n",
       "public_service_8           0\n",
       "public_service_9           0\n",
       "Length: 140, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断是否包含缺失值\n",
    "# isnull用于遍历dataframe每一个元素，将是空的元素置为True，将非空的元素置为False，sum用于计算每一列中为True元素的个数\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loc[]方法用于实现将happiness=-8的行进行过滤，也就是保留hapiness不为-8的行，因为幸福指数只有1-5，-8明显是一个错误的读入值\n",
    "train = train.loc[train['happiness'] != -8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 140)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    4818\n",
       "5    1410\n",
       "3    1171\n",
       "2     497\n",
       "1     104\n",
       "Name: happiness, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['happiness'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用.dt.year将survey_time转换成year的时间\n",
    "train['survey_time'] = train['survey_time'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过birth和survey时间计算出接受采访者的年龄\n",
    "train['Age'] = train['survey_time']-train['birth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       56\n",
      "1       23\n",
      "2       48\n",
      "3       72\n",
      "4       21\n",
      "5       69\n",
      "6       52\n",
      "7       56\n",
      "8       63\n",
      "9       30\n",
      "10      27\n",
      "11      47\n",
      "12      51\n",
      "13      76\n",
      "14      80\n",
      "15      32\n",
      "16      41\n",
      "17      53\n",
      "18      39\n",
      "19      50\n",
      "20      64\n",
      "21      67\n",
      "22      35\n",
      "23      73\n",
      "24      59\n",
      "25      45\n",
      "26      49\n",
      "27      52\n",
      "28      23\n",
      "29      68\n",
      "        ..\n",
      "7970    57\n",
      "7971    20\n",
      "7972    38\n",
      "7973    39\n",
      "7974    50\n",
      "7975    43\n",
      "7976    62\n",
      "7977    67\n",
      "7978    39\n",
      "7979    31\n",
      "7980    61\n",
      "7981    69\n",
      "7982    46\n",
      "7983    62\n",
      "7984    80\n",
      "7985    45\n",
      "7986    40\n",
      "7987    52\n",
      "7988    54\n",
      "7989    84\n",
      "7990    25\n",
      "7991    43\n",
      "7992    49\n",
      "7993    61\n",
      "7994    40\n",
      "7995    34\n",
      "7996    70\n",
      "7997    48\n",
      "7998    37\n",
      "7999    24\n",
      "Name: Age, Length: 8000, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "del_list=['survey_time','birth']\n",
    "print(train['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "对dataframe中的数据进行简单的编码\n",
    "小于16岁的为0；16-32的为1；32-48的为2；48-64的为3；64-80的为4；大于80的为5\n",
    "'''\n",
    "combine=[train]\n",
    "for dataset in combine:\n",
    "    dataset.loc[dataset['Age']<=16,'Age']=0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[(dataset['Age'] > 64) & (dataset['Age'] <= 80), 'Age'] = 4\n",
    "    dataset.loc[ dataset['Age'] > 80, 'Age'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Administrator\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "绘制以下数据的热度图，\n",
    ".corr()方法表示的是计算dataframe多个指标的相关系数矩阵，默认使用pearson计算方法\n",
    "train[]表示传入热度图的数据，\n",
    "annot（布尔类型），用于控制是否在个字中间标明数字，\n",
    "cmap表示控制热度的渐变色，\n",
    "linewidths表示每个单元格的线的宽度'''\n",
    "sns.heatmap(train[['happiness','Age','inc_ability','gender','status_peer','family_status','health','equity','class','work_exper','health_problem','family_m','house','depression','learn','relax','edu']].corr(),annot=True,cmap='RdYlGn',linewidths=0.2) #data.corr()-->correlation matrix\n",
    "fig=plt.gcf()  #获取当前的图表和子图\n",
    "fig.set_size_inches(15,15)  #设置图像的密集度：设置图像的长和宽\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "最后选择的特征为Age年龄,inc_ability收入是否合理,gender性别,status_peer与同龄人相比的收入情况,work_exper工作经历及情况,family_status家庭年收入情况,health身体健康状况,equity认为社会是否公平,class认为应该处于的社会阶层,health_problem影响健康的程度,family_m家庭人数,house拥有房产数量,depression压力沮丧程度,learn是否学习充电,relax休闲放松,edu教育程度\n",
    "'''\n",
    "features=['Age','inc_ability','gender','status_peer','work_exper','family_status','health','equity','class','health_problem','family_m','house','depression','learn','relax','edu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置训练模型参数，target为模型输出的标签项，train为模型输入，test为测试集数据\n",
    "# 特征工程完成得到最后用于训练和测试的数据集\n",
    "train_target = train['happiness']\n",
    "train = train[features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二、数据测试集和训练集划分及归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 进行数据随机划分模块\n",
    "from sklearn.model_selection import train_test_split \n",
    "# 进行数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "standardScaler = StandardScaler()\n",
    "standardScaler.fit(train)\n",
    "train_transform = standardScaler.transform(train)\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(train_transform,train_target,test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、训练knn模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先引入进行评估的相关的包\n",
    "from sklearn import metrics    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5591666666666667 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "test_knn = KNeighborsClassifier(n_neighbors = 5,p = 2,weights = 'distance')\n",
    "test_knn.fit(X_train,Y_train)\n",
    "Y_predict_train = test_knn.predict(X_test)\n",
    "print(metrics.accuracy_score(Y_test,Y_predict_train),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四、训练BP神经网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLPClassifier() BP神经网络\n",
    "# 查看文章：https://blog.csdn.net/weixin_42348202/article/details/100568469\n",
    "from sklearn.neural_network import MLPClassifier   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5870833333333333 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "bp = MLPClassifier(hidden_layer_sizes=(50,50),max_iter=100)\n",
    "bp.fit(X_train,Y_train)\n",
    "Y_bp_predict = bp.predict(X_test)\n",
    "print(metrics.accuracy_score(Y_test,Y_bp_predict),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 五、训练决策树模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.595 %\n"
     ]
    }
   ],
   "source": [
    "# 导入需要的块\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=7,min_samples_split=4)\n",
    "tree.fit(X_train,Y_train)\n",
    "Y_tree_predict = tree.predict(X_test)\n",
    "print(metrics.accuracy_score(Y_tree_predict,Y_test),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 六、使用遗传算法，进行进化学习，得到每个决策分类模型中表现较好的7个参数模型，得到一共21个模型，最后进行集成投票的原则得到更好的分类结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一些全局变量，方便变异操作\n",
    "# 所有模型对应的范围\n",
    "# knn模型对应的范围\n",
    "knn_n_neighbors_min = 5\n",
    "knn_n_neighbors_max = 30\n",
    "knn_p_min = 1\n",
    "knn_p_max = 6\n",
    "# bp神经网络模型参数对应范围\n",
    "bp_iter_min = 100\n",
    "bp_iter_max = 300\n",
    "bp_x_min = 10\n",
    "bp_x_max = 50\n",
    "bp_y_min = 10\n",
    "bp_y_max = 50\n",
    "# 决策树模型参数对应的范围\n",
    "tree_depth_min = 5\n",
    "tree_depth_max = 15\n",
    "tree_split_min = 2\n",
    "tree_split_max = 8\n",
    "#迭代的次数：\n",
    "epoches = 200\n",
    "# 相应的分类器集合和参数集合，设置为全局变量\n",
    "knn_set = []\n",
    "bp_set = []\n",
    "tree_set = []\n",
    "knn_param_set = []\n",
    "bp_param_set = []\n",
    "tree_param_set = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1、确定初始群体，进行初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init():\n",
    "    #knn初始化群体\n",
    "    for n_neighbors in range(5,31,2):\n",
    "        for p in range(1,6,1):\n",
    "            knn_set.append(KNeighborsClassifier(n_neighbors =n_neighbors ,p = p))\n",
    "            knn_param_set.append([n_neighbors,p])\n",
    "    for max_iter in range(100,300,20):\n",
    "        for x in range(10,51,5):\n",
    "            for y in range(10,51,5):\n",
    "                bp_set.append(MLPClassifier(hidden_layer_sizes=(x,y),max_iter=max_iter))\n",
    "                bp_param_set.append([max_iter,x,y])\n",
    "    for max_depth in range(5,15,1):\n",
    "        for min_samples_split in range(2,8,1):\n",
    "            tree_set.append(DecisionTreeClassifier(max_depth=max_depth,min_samples_split=min_samples_split))\n",
    "            tree_param_set.append([max_depth,min_samples_split])\n",
    "    return knn_set,bp_set,tree_set,knn_param_set,bp_param_set,tree_param_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2、选择，使用准确率作为评分依据得到累计概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 得到评分\n",
    "def classfier_score(X,Y,classfier):\n",
    "    classfier.fit(X, Y)\n",
    "    pred = classfier.predict(X)\n",
    "    return metrics.accuracy_score(y_true=Y, y_pred=pred)\n",
    "\n",
    "# 计算每个个体的适应度,返回适应度圆盘，用于下一步轮盘赌原则用于选择个体\n",
    "def adaption(X,Y,classfier_set):\n",
    "    score = []\n",
    "    for classfier in classfier_set:\n",
    "        score.append(classfier_score(X,Y,classfier))\n",
    "    sm = np.sum(score)\n",
    "    ada = score / sm\n",
    "    for i in range(1, len(ada)):\n",
    "        ada[i] = ada[i] + ada[i - 1]\n",
    "    return ada\n",
    "                               \n",
    "# 根据轮盘进行选择,得到对应的分类器和对应的分类参数\n",
    "def choose_classfiers(classfier_set,classfier_param_set,ada):\n",
    "    count = len(classfier_set)\n",
    "    result = []\n",
    "    result_param =[]\n",
    "    for i in range(count):\n",
    "        r = random.random()\n",
    "        for j in range(len(ada)):\n",
    "            if r<= ada[j]:\n",
    "                result.append(classfier_set[j])\n",
    "                result_param.append(classfier_param_set[j])\n",
    "                break;\n",
    "    return result,result_param                      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3、交叉，使用的是参数的交叉，随机根据参数的位置进行，交叉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 两个分类器参数之间的交叉，分类型进行不用类别的交叉,\n",
    "def cross(index1,index2,type,param_set):\n",
    "    if (type == \"knn\") or (type == \"tree\"):\n",
    "        #随机产生0-1的数进行交叉\n",
    "        index_temp = random.randint(0,1)\n",
    "        data_temp = param_set[index1][index_temp]\n",
    "        param_set[index1][index_temp] = param_set[index2][index_temp]\n",
    "        param_set[index2][index_temp] = data_temp\n",
    "    else:\n",
    "        # 随机产生0-2的树进行交叉\n",
    "        index_temp = random.randint(0,2)\n",
    "        data_temp = param_set[index1][index_temp]\n",
    "        param_set[index1][index_temp] = param_set[index2][index_temp]\n",
    "        param_set[index2][index_temp] = data_temp\n",
    "    return\n",
    "\n",
    "# 整个集合的交叉也要传进来类型是什么，对于该集合的参数，每间隔三个个体进行随机交叉\n",
    "def all_cross(classfier_set,type):\n",
    "    set_len = len(classfier_set)\n",
    "    # 最后交叉完的集合\n",
    "    for i in range(1,set_len,3):\n",
    "        cross(i,i-1,type,classfier_set)\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4、变异，通过对参数进行操作达到变异的目的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分类型进行不同类别的变异，变异的范围还要进行自己的确认不能超出正常范围,变异率默认设置为0.2\n",
    "# 就是简单的+1或者-1操作，当达到最大值时只能减，最小值时只能加，其他随意加减\n",
    "def variation(classfier_set,type,probality):\n",
    "    count = int(len(classfier_set)*probality)#变异的个数\n",
    "    while count > 0:\n",
    "        index_tep = random.randint(0,len(classfier_set))#每个分类器变异的位置\n",
    "        operation = random.randint(0,1)#0减1加\n",
    "        if type == 'knn':\n",
    "            index_param = random.randint(0,1)#参数变异的位置\n",
    "            if operation == 0:\n",
    "                if index_param == 0:\n",
    "                    if classfier_set[index_tep][0] == knn_n_neighbors_min:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] +1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] -1\n",
    "                else:\n",
    "                    if classfier_set[index_tep][1] == knn_p_min:\n",
    "                        classfier_set[index_tep][1] = classfier_set[index_tep][1] + 1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][1]= classfier_set[index_tep][1] - 1\n",
    "            else:#运算是加的情况\n",
    "                if index_param == 0:\n",
    "                    if classfier_set[index_tep][0] == knn_n_neighbors_max:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] -1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] +1\n",
    "                else:\n",
    "                    if classfier_set[index_tep][1] == knn_p_max:\n",
    "                        classfier_set[index_tep][1] = classfier_set[index_tep][1] -1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][1]= classfier_set[index_tep][1] + 1\n",
    "        elif type ==\"tree\":\n",
    "            index_param = random.randint(0,1)#参数变异的位置\n",
    "            if operation == 0:\n",
    "                if index_param == 0:\n",
    "                    if classfier_set[index_tep][0] == tree_depth_min:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] +1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] -1\n",
    "                else:\n",
    "                    if classfier_set[index_tep][1] == tree_split_min:\n",
    "                        classfier_set[index_tep][1] = classfier_set[index_tep][1] + 1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][1]= classfier_set[index_tep][1] - 1\n",
    "            else:#运算是加的情况\n",
    "                if index_param == 0:\n",
    "                    if classfier_set[index_tep][0] == tree_depth_max:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] -1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] +1\n",
    "                else:\n",
    "                    if classfier_set[index_tep][1] == tree_split_max:\n",
    "                        classfier_set[index_tep][1] = classfier_set[index_tep][1] -1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][1]= classfier_set[index_tep][1] + 1  \n",
    "        else:#是神经网络模型的时候\n",
    "            index_param = random.randint(0,2)#参数变异的位置\n",
    "            if operation == 0:\n",
    "                if index_param == 0:\n",
    "                    if classfier_set[index_tep][0] == bp_iter_min:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] +1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] -1\n",
    "                elif index_param == 1:\n",
    "                    if classfier_set[index_tep][1] == bp_x_min:\n",
    "                        classfier_set[index_tep][1] = classfier_set[index_tep][1] + 1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][1]= classfier_set[index_tep][1] - 1\n",
    "                else:\n",
    "                    if classfier_set[index_tep][2] == bp_y_min:\n",
    "                        classfier_set[index_tep][2] = classfier_set[index_tep][2] + 1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][2]= classfier_set[index_tep][2] - 1    \n",
    "            else:#运算是加的情况\n",
    "                if index_param == 0:\n",
    "                    if classfier_set[index_tep][0] == bp_iter_max:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] -1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][0] = classfier_set[index_tep][0] +1\n",
    "                elif index_param == 1:\n",
    "                    if classfier_set[index_tep][1] == bp_x_max:\n",
    "                        classfier_set[index_tep][1] = classfier_set[index_tep][1] -1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][1]= classfier_set[index_tep][1] + 1\n",
    "                else:\n",
    "                    if classfier_set[index_tep][2] == bp_y_max:\n",
    "                        classfier_set[index_tep][2] = classfier_set[index_tep][2] - 1\n",
    "                    else:\n",
    "                        classfier_set[index_tep][2]= classfier_set[index_tep][2] + 1         \n",
    "        count = count - 1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 更新相应的分类器参数后，继续根据参数，得到相应的分类器集合\n",
    "def to_get_classfiers_set():\n",
    "    new_knn_set = []\n",
    "    new_bp_set = []\n",
    "    new_tree_set = []\n",
    "    for temp in knn_param_set:\n",
    "        new_knn_set.append(KNeighbor,sClassifier(n_neighbors =temp[0] ,p = temp[1]))\n",
    "    for temp1 in bp_param_set:\n",
    "        new_bp_set.append(MLPClassifier(hidden_layer_sizes=(temp1[1],temp1[2]),max_iter=temp[0]))\n",
    "    for temp2 in tree_param_set:\n",
    "        new_tree_set.append(DecisionTreeClassifier(max_depth=temp2[0],min_samples_split=temp2[1]))\n",
    "    return new_knn_set,new_bp_set,new_tree_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5、进化大约200次最后得到21个较优的个体，最后得到综合的分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 迭代200次得到每个类别进化的个体集合，从每个集合选择准确率最高的7个\n",
    "def to_get_best(knn_set,bp_set,tree_set):\n",
    "    knn_best_set = set()\n",
    "    bp_best_set = set()\n",
    "    tree_best_set = set()\n",
    "    min_score = 1#记录当前集合最小分数\n",
    "    min_classfier = 0 #记录最小评估分数的分类器\n",
    "    for i in range(0,7):\n",
    "        for temp_classfier in knn_set:\n",
    "            if len(knn_best_set) <= 7:\n",
    "                temp_score = classfier_score(X_train,X_test,temp_classfier)\n",
    "                if temp_score < min_score:\n",
    "                    min_classfier = temp_classfier \n",
    "                knn_best_set.add(temp_classfier)\n",
    "            else:\n",
    "                temp_score = classfier_score(X_train,X_test,temp_classfier)\n",
    "                if temp_score< min_score:\n",
    "                    knn_best_set.remove(min_classfier)\n",
    "                    knn_best_set.add(temp_classfier)\n",
    "                    min_score = temp_score\n",
    "        for temp_classfier in bp_set:\n",
    "            if len(bp_best_set) <= 7:\n",
    "                temp_score = classfier_score(X_train,X_test,temp_classfier)\n",
    "                if temp_score < min_score:\n",
    "                    min_classfier = temp_classfier \n",
    "                bp_best_set.add(temp_classfier)\n",
    "            else:\n",
    "                temp_score = classfier_score(X_train,X_test,temp_classfier)\n",
    "                if temp_score< min_score:\n",
    "                    bp_best_set.remove(min_classfier)\n",
    "                    bp_best_set.add(temp_classfier)\n",
    "                    min_score = temp_score\n",
    "        for temp_classfier in tree_set:\n",
    "            if len(knn_best_set) <= 7:\n",
    "                temp_score = classfier_score(X_train,X_test,temp_classfier)\n",
    "                if temp_score < min_score:\n",
    "                    min_classfier = temp_classfier \n",
    "                tree_best_set.add(temp_classfier)\n",
    "            else:\n",
    "                temp_score = classfier_score(X_train,X_test,temp_classfier)\n",
    "                if temp_score< min_score:\n",
    "                    tree_best_set.remove(min_classfier)\n",
    "                    tree_best_set.add(temp_classfier)\n",
    "                    min_score = temp_score\n",
    "        the_end_set = set.union(knn_best_set,bp_best_set,tree_best_set)\n",
    "    return the_end_set\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 6、根据现在所有的模型，进行投票集成学习得到最后所有元素的分类，并且返回准确率\n",
    "def to_ensemble(end_classfiers_set):\n",
    "    y_label_predict = []\n",
    "    for temp_data in X_test:\n",
    "        predict_set = {}\n",
    "        for classfiers in end_classfiers_set:\n",
    "            classfiers.fit(X_train,Y_train)\n",
    "            predict_set.append(classfiers.predict(temp_data))\n",
    "            if classfiers not in predict_set.keys:\n",
    "                predict_set[classfiers] = 1\n",
    "            else:\n",
    "                predict_set[classfiers] = predict_set[classfiers] + 1\n",
    "        label = max(predict_set, key=predict_set.get)\n",
    "        y_label_predict.append(label)\n",
    "    return metrics.accuracy_score(y_label_predict,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 七、最后进行模型综合后，进行最后的评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 利用最后得到的最优的21个个体最终得到最后的模型\n",
    "# 主函数入口\n",
    "def main():\n",
    "    #初始化\n",
    "    global epoches\n",
    "    knn_set,bp_set,tree_set,knn_param_set,bp_param_set,tree_param_set = init()\n",
    "    while epoches > 0 :\n",
    "        #选择\n",
    "        ada1 = adaption(X_train,Y_train,knn_set)\n",
    "        knn_set,knn_param_set = choose_classfiers(knn_set,knn_param_set,ada1)\n",
    "        ada2 = adaption(X_train,Y_train,bp_set)\n",
    "        bp_set,bp_param_set = choose_classfiers(bp_set,bp_param_set,ada2)\n",
    "        ada3 = adaption(X_train,Y_train,tree_set)\n",
    "        tree_set,tree_param_set = choose_classfiers(tree_set,tree_param_set,ada3)\n",
    "        #交叉\n",
    "        all_cross(knn_param_set,\"knn\")\n",
    "        all_cross(bp_param_set,\"bp\")\n",
    "        all_cross(tree_param_set,\"tree\")\n",
    "        #变异\n",
    "        variation(knn_param_set,\"knn\",0.2)\n",
    "        variation(bp_param_set,\"bp\",0.2)\n",
    "        variation(tree_param_set,\"tree\",0.2)\n",
    "        #更新分类集合\n",
    "        knn_set,bp_set,tree_set = to_get_classfiers_set()\n",
    "        epoches = epoches -1 \n",
    "    #得到综合分类器\n",
    "    the_end_set = to_get_best(knn_set,bp_set,tree_set)\n",
    "    accuracy = to_ensemble(the_end_set)\n",
    "    print(\"准确率是：\",accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "main()"
   ]
  }
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